Abstract
Diabetic foot ulcers (DFU) are one of the most frequent and debilitating complications of diabetes. DFU wound healing is a highly complex process, resulting in significant medical, economic and social challenges. Therefore, early identification of patients with a high-risk profile would be important to adequate treatment and more successful health outcomes. This study explores risk assessment profiles for DFU healing and healing prognosis, using machine learning predictive approaches and decision tree algorithms. Patients were evaluated at baseline (T0; N = 158) and 2 months later (T1; N = 108) on sociodemographic, clinical, biochemical and psychological variables. The performance evaluation of the models comprised F1-score, accuracy, precision and recall. Only profiles with F1-score >0.7 were selected for analysis. According to the two profiles generated for DFU healing, the most important predictive factors were illness representations on T1 IPQ-B (IPQ-B <= 9.5 and < 10.5) and the DFU duration (<= 13 weeks). The two predictive models for DFU healing prognosis suggest that biochemical factors are the best predictors of a favorable healing prognosis, namely IL-6, microRNA-146a-5p and PECAM-1 at T0 and angiopoietin-2 at T1. Illness perception at T0 (IPQ-B <= 39.5) also emerged as a relevant predictor for healing prognosis. The results emphasize the importance of DFU duration, illness perception and biochemical markers as predictors of healing in chronic DFUs. Future research is needed to confirm and test the obtained predictive models.

Abstract
This paper presents several case studies that show that it is possible to use clean energy to produce electricity, we have environmental benefits and benefits for the management of the electrical transmission network. In this case wind energy are used.For this work, software was developed in Matlab for the model we developed and the results of this were compared with the results obtained by the simulator Power World.To make the decision to replace generators fossil generators with wind generators, Local Marginal Prices (LMP) were used. Some case studies were created using a model system, with the objective of evaluating the benefits of this allocation based on the LMP.The test network presented in this paper is a 9 Bus network. However, the developed software was also tested on an IEEE 30 Bus network. © 2023 IEEE.

Abstract
The development of increasingly autonomous underwater vehicles has long been a research focus in underwater robotics. Recent advances in deep learning have shown promising results, offering the potential for fully autonomous behavior in underwater vehicles. However, its implementation requires improvements to the current vehicles. This paper proposes an onboard data processing framework for Deep Learning implementation. The proposed framework aims to increase the autonomy of the vehicles by allowing them to interact with their environment in real time, enabling real-time detection, control, and navigation.

Abstract
This paper presents a roadmap to the application of AI techniques and big data (BD) for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and BD techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10-20 yr time-frame.

Abstract
In this article the influence of the maximum partition size on the performance of a discrete version of the Bat Algorithm (BA) is studied. The Bat Algorithm is a population-based meta-heuristic based on swarm intelligence developed for continuous problems with exceptional results. Thus, it has a set of parameters that must be studied in order to enhance the performance of the meta-heuristic. This paper aims to investigate whether the maximum size of the partitions used for the search operations throughout the algorithm should not also be considered as a parameter. First, a literature review was conducted, with special focus on the parameterization of the meta-heuristics and each of the parameters currently used in the algorithm, followed by its implementation in VBA in Microsoft Excel. After a thorough parameterization of the discrete algorithm, different maximum partition sizes were applied to 30 normally distributed instances to draw broader conclusions. In addition, they were also tested for different sizes of the problem to see if they had an influence on the results obtained. Finally, a statistical analysis was carried out, where it was possible to conclude that there was no maximum partition value for which superiority could be proven, and so the size of the partition should be considered a parameter in the bat algorithm and included in the parametrization of BA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The transport and logistics sector, which include freight forwarders companies, constitutes a vast network of entities that are central to a good performance in services. With the COVID-19 pandemic and its effects on the global economy, there was a huge shortage in the number of containers available, thus creating the need to optimize the loading of available equipment to avoid waste and maximize profits from each export. The present work presents a novel approach where a set of restrictions were created that, applied in synergy with the Non-Linear GRG algorithm, aim to allocate the boxes in different consecutive lines until forming a wall, and, therefore, the walls complete the container, in order to maximize the occupancy on it. To validate the proposed approach a prototype was developed and studied in real-world problem where the solutions resulted in occupations around 80% to 90%. Thus, we can foresee the importance of the proposed approach in decision-making regarding container consolidation services. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.